01664nas a2200169   4500008004100000022001400041245011000055210006900165300000800234490000600242520112900248100001701377700001901394700001701413700002101430856004301451       2021                        eng d  a2077-131200aHull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing0 aHull Shape Design Optimization with Parameter Space and Model Re  a1850 v93 a<p>In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship.</p>1 aDemo, Nicola1 aTezzele, Marco1 aMola, Andrea1 aRozza, Gianluigi  uhttps://www.mdpi.com/2077-1312/9/2/185